A network performance evaluation method and device based on swept frequency data

Abstract

The present invention discloses a method and device for network performance evaluation based on sweep data. The method includes: collecting sweep data of a scene to be evaluated, dividing the scene to be evaluated into data blocks according to a preset distance, and generating a set of data reference tables after processing the sweep data according to the data blocks; generating a frequency point set of the data blocks based on the set of data reference tables; extracting the frequency point information in the set of data reference tables and respectively matching it with the frequency point set of the data blocks, and filling the frequency point set of the data blocks according to the matching results; evaluating the network performance of the scene to be evaluated based on the filled frequency point set of the data blocks to obtain a network performance evaluation result. The present invention uses sweep data to evaluate the network capacity performance on the basis of analyzing the coverage interference situation in high-speed rail and subway scenes, and solves the technical problem in the prior art that the capacity situation along the track area cannot be reflected according to the coverage.

Description

Technical Field

[0001] The present invention relates to the field of communication technologies, and in particular, to a method and apparatus for network performance evaluation based on sweep data. Background Art

[0002] In recent years, the operating line mileage of high-speed rail and subway scenarios in China has increased significantly, the passenger flow has increased rapidly, and at the same time, users' requirements for the quality of wireless network services have become higher and higher. In the early network deployment, coverage performance was usually given priority in high-speed rail and subway scenarios. However, with the increasing number of mobile users, the network capacity problem has become the focus of attention of the network system performance indicators in these two scenarios.

[0003] The data of the scenario capacity evaluation indicators in the prior art are mainly the cell-level traffic statistics data extracted by network management, including indicators such as the maximum number of users and the maximum number of active users. The capacity expansion threshold of this scenario is based on the maximum number of connected users of Radio Resource Control (RRC) as the basic basis, and the specific judgment threshold needs to be comprehensively calculated according to the upper limit of the device performance specifications, the actual carrying capacity of the device, and the user service perception.

[0004] However, the above method for evaluating the capacity of high-speed rail and subway scenarios based on the cell-level traffic statistics data extracted by network management has the following problems: First, using the cell-level traffic statistics data as the indicator data cannot accurately evaluate the scenario capacity. This is because the existing capacity evaluation indicators for high-speed rail and subway scenarios are mainly based on the cell-level traffic statistics data. However, high-speed rail and subway include coverage scenarios such as platforms and track areas. Among them, the track area is a chain-connected area. For this scenario, high-speed rail (mostly outdoor) uses fiber optic remote networking and cell merging methods to solve the problem, while subway (mostly tunnels) introduces various passive devices such as a Point of Interface (POI) and leaky cables to solve the problem. In this case, using the cell-level indicator data cannot reflect the capacity performance indicators of sub-cells or devices such as POIs, and the capacity situation along the track area cannot be reflected according to the coverage. Second, it cannot reflect the capacity problems caused by hardware device failures. This is because for the overall cell indicator data, the capacity problems caused by hardware failures of a certain sub-cell or POI device cannot be monitored and processed. Summary of the Invention

[0005] In view of the above problems, the present invention is proposed to provide a method and apparatus for network performance evaluation based on sweep data that can overcome or at least partially solve the above problems.

[0006] According to one aspect of the present invention, there is provided a method for network performance evaluation based on sweep data, including:

[0007] Collect the sweep frequency data of the scene to be evaluated, divide the scene to be evaluated into data blocks according to a preset distance, and generate a set of data benchmark tables after processing the sweep frequency data according to the data blocks;

[0008] Generate a data block frequency point set based on the set of data benchmark tables;

[0009] Extract the frequency point information in the set of data benchmark tables and match them with the data block frequency point set respectively, and fill the data block frequency point set according to the matching results;

[0010] Evaluate the network performance of the scene to be evaluated based on the filled data block frequency point set to obtain a network performance evaluation result.

[0011] According to another aspect of the present invention, there is provided a network performance evaluation device based on sweep frequency data, including:

[0012] A data set construction module, configured to collect the sweep frequency data of the scene to be evaluated, divide the scene to be evaluated into data blocks according to a preset distance, generate a set of data benchmark tables after processing the sweep frequency data according to the data blocks; generate a data block frequency point set based on the set of data benchmark tables;

[0013] A filling module, configured to extract the frequency point information in the set of data benchmark tables and match them with the data block frequency point set respectively, and fill the data block frequency point set according to the matching results;

[0014] An evaluation module, configured to evaluate the network performance of the scene to be evaluated based on the filled data block frequency point set to obtain a network performance evaluation result.

[0015] According to still another aspect of the present invention, there is provided a computing device, including: a processor, a memory, a communication interface, and a communication bus, and the processor, the memory, and the communication interface complete communication with each other through the communication bus;

[0016] The memory is used to store at least one executable instruction, and the executable instruction causes the processor to perform the operations corresponding to the above-mentioned network performance evaluation method based on sweep frequency data.

[0017] According to yet another aspect of the present invention, there is provided a computer storage medium, and at least one executable instruction is stored in the storage medium, and the executable instruction causes the processor to perform the operations corresponding to the above-mentioned network performance evaluation method based on sweep frequency data.

[0018] A network performance evaluation method based on sweep frequency data according to the present invention collects sweep frequency data of a scene to be evaluated, divides the scene to be evaluated into data blocks according to a preset distance, processes the sweep frequency data according to the data blocks to generate a set of data reference tables; generates a frequency point set of data blocks according to the set of data reference tables; extracts frequency point information in the set of data reference tables and matches it with the frequency point set of data blocks respectively, and fills the frequency point set of data blocks according to the matching result; evaluates the network performance of the scene to be evaluated according to the filled frequency point set of data blocks to obtain a network performance evaluation result. The present invention uses sweep frequency data to evaluate the network capacity performance on the basis of analyzing the coverage interference situation of high-speed rail and subway scenes, and solves the technical problem in the prior art that the capacity situation along the track area cannot be reflected according to the coverage.

[0019] The above description is only an overview of the technical solution of the present invention. In order to be able to understand the technical means of the present invention more clearly, it can be implemented according to the content of the specification. And in order to make the above and other purposes, features and advantages of the present invention more obvious and understandable, the following specifically illustrates the specific embodiments of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] By reading the following detailed description of the preferred embodiments, various other advantages and benefits will become clear to those of ordinary skill in the art. The drawings are only for the purpose of showing the preferred embodiments and are not considered to be a limitation of the present invention. And throughout the drawings, the same reference numerals are used to represent the same components. In the drawings:

[0021] Figure 1 Shows a flowchart of a network performance evaluation method based on sweep frequency data provided by an embodiment of the present invention;

[0022] Figure 2 Shows a schematic diagram of sweep frequency data provided by an embodiment of the present invention;

[0023] Figure 3 Shows a schematic diagram of the processed sweep frequency data provided by an embodiment of the present invention;

[0024] Figure 4 Shows a schematic diagram of a set of data reference tables JZ provided by an embodiment of the present invention;

[0025] Figure 5 Shows a schematic diagram of an initial table of a frequency point set JZ_A of data blocks provided by an embodiment of the present invention;

[0026] Figure 6 Shows a schematic diagram of the frequency point set of data blocks after filling provided by an embodiment of the present invention;

[0027] Figure 7 Shows a schematic diagram of capacity continuity scoring provided by an embodiment of the present invention;

[0028] Figure 8 Shows a schematic diagram of the calculation result of the capacity thickness provided by the embodiment of the present invention;

[0029] Figure 9 Shows a schematic diagram of the calculation result of the capacity continuity provided by the embodiment of the present invention;

[0030] Figure 10 Shows a schematic diagram of the calculation result of the equivalent capacity thickness provided by the embodiment of the present invention;

[0031] Figure 11 Shows a schematic diagram of the structure of a network performance evaluation device based on sweep frequency data provided by the embodiment of the present invention;

[0032] Figure 12 Shows a schematic diagram of the structure of the computing device provided by the embodiment of the present invention. Specific embodiments

[0033] Hereinafter, exemplary embodiments of the present invention will be described in more detail with reference to the drawings. Although the exemplary embodiments of the present invention are shown in the drawings, it should be understood that the present invention can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided so that the present invention can be more thoroughly understood and the scope of the present invention can be completely conveyed to those skilled in the art.

[0034] Figure 1 Shows a flowchart of an embodiment of a network performance evaluation method based on sweep frequency data of the present invention. As Figure 1 shown, the method includes the following steps:

[0035] Step S110: Collect sweep frequency data of the scene to be evaluated, divide the scene to be evaluated into data blocks according to a preset distance, and generate a set of data reference tables after processing the sweep frequency data according to the data blocks.

[0036] In an optional manner, the sweep frequency data at least includes frequency point information of the signal, physical cell identification information, and level intensity information; step S110 further includes: for each data block, select the characteristic level value of the data block according to the level intensity information of the signal in the data block; perform data processing on the sweep frequency data according to the characteristic level value, and generate a set of data reference tables according to the processed sweep frequency data.

[0037] Figure 2 Is an example of the collected sweep frequency data. As Figure 2As shown, the swept frequency data includes: timestamp data, longitude data, latitude data, frequency point (EARFCN) information, PCI information, received signal strength indication (RSSI) information of the secondary synchronization signal (SSS), level strength (RO_RP) information, etc. For Figure 2 the swept frequency data in, in this embodiment, the mainly used are frequency point information, PCI information and level strength information. Specifically, the scene to be evaluated is divided into data blocks, for example, divided into data blocks according to a preset distance (such as 500 meters), and the swept frequency data preprocessing is carried out with the divided data blocks as the granularity. Specifically, the maximum value of the level strength of the same signal is retained as the characteristic level value of the data block, and then according to the preset level strength value, the signal information of the data blocks that meet the conditions is retained. (For example, the preset level strength value is -95 dBm, that is, the signals with a level strength value above -95 dBm are retained), and the processed swept frequency data is obtained as Figure 3 shown; at the same time, the minimum continuous coverage distance is set according to the distance of the continuous private network (such as 1500 meters, that is, the minimum continuous coverage distance for private network cell determination is greater than or equal to 1500 meters), and only the private network cells are retained to generate the data reference table set JZ. Taking a certain data as an example, the data reference table set JZ is obtained as Figure 4 shown, where cell1-cell15 represent 15 cells, and the unit of the distance label is meters. Each cell in this data reference table set JZ includes frequency point information and PCI information. Taking Figure 4 the F2 cell in as an example: 1300-416, where 1300 is the frequency point and 416 is the PCI information. The frequency point information and PCI information are two data items that match and characterize the cell.

[0038] Step S120: Generate a data block frequency point set according to the data reference table set.

[0039] In an alternative way, step S120 further includes: extracting the frequency point information of the swept frequency data corresponding to multiple data blocks from the data reference table set, generating subsets of frequency points of multiple data blocks according to the data blocks, and constructing a data block frequency point set according to the subsets of frequency points of multiple data blocks.

[0040] Figure 5It is a schematic diagram of the initial table of the frequency point set JZ_A of data blocks. Specifically, after extracting the frequency point information of multiple cells from the data reference table set JZ, frequency point subsets of multiple data blocks are generated, and the frequency point set JZ_A of data blocks is constructed based on the frequency point subsets of multiple data blocks. At this time, due to conditions such as the pre-set characteristic level value and the size of the data block distance, multiple frequency point information may be missing. Generally, the coverage of a cell should be continuous. In order to exclude individual abnormal test points that cause low signal indicators of the cell in the data block, the frequency point set JZ_A of data blocks can be filled by complementing the frequency point information. Specifically, it can be automatically filled and complemented through the setting items of the interface.

[0041] Step S130: Extract the frequency point information in the data reference table set and match it with the frequency point set of data blocks respectively, and fill the frequency point set of data blocks according to the matching result.

[0042] In an optional manner, step S130 further includes: for each data block, extract the frequency point information corresponding to the data block in the data reference table set and match it with the frequency point subset of the data block in the frequency point set of data blocks, and determine whether the frequency point information already exists in the frequency point subset of the data block. If so, perform a marking process on the frequency point information in the data reference table set; if not, write the frequency point information into the frequency point subset of the data block.

[0043] Specifically, Figure 6 It is a schematic diagram of the frequency point set of data blocks after filling. As Figure 6 shown, take out the i-th row of data from the data reference table set JZ, that is, the sweep frequency data JZ[i] of a certain data block. The cell information set of JZ[i] is JZ[i]_C[j], where A[j] is the frequency point information, P[j] is the PCI information. Extract the frequency point information A[j] of each signal in the frequency point subset of this data block, and determine whether the frequency point information A[j] already exists in the frequency point subset of this data block. If not, write it into the corresponding data block of the frequency point set JZ_A of data blocks; if the frequency point information A[j] already exists, discard the frequency point information A[j], and perform a marking process on the frequency point information in the data reference table set. Loop through the above operations to complete the writing of all frequency point information of this data block.

[0044] Step S140: Evaluate the network performance of the to-be-evaluated scenario based on the filled frequency point set of data blocks to obtain a network performance evaluation result.

[0045] In an alternative approach, step S140 further includes: for each frequency point, scoring the capacity continuity of the frequency point based on its occurrence in the frequency point subsets of each data block, and generating a capacity continuity scoring table according to the capacity continuity scores of each frequency point; calculating the index data of the scenario to be evaluated based on the capacity continuity scoring table and the frequency point set of the filled data blocks; evaluating the network performance of the scenario to be evaluated based on the index data to obtain a network performance evaluation result.

[0046] Figure 7 Figure for scoring capacity continuity, as Figure 7 shown, scoring the capacity continuity of the frequency point based on its occurrence in the frequency point subsets of each data block to evaluate whether the capacity layer can achieve continuous coverage. The specific scoring principles are as follows:

[0047] Principle 1: Score at the regional level (such as city) according to the regional label. If the frequency point information label of the previous group exists continuously in the next group of data blocks, add one point; if not, subtract one point.

[0048] Principle 2: If it does not exist in the previous group of data blocks and appears for the first time in the current group of data blocks, add one point.

[0049] Principle 3: When there is an overlap in the regional labels, score in the form of a new data block.

[0050] Figure 7 Only individual examples are given in , and the capacity continuity scores of all distance labels are generated according to the above Principles 1-3 to generate a capacity continuity scoring table.

[0051] In an alternative approach, the index data includes one or more of the following data: capacity thickness, capacity continuity, signal coverage rate, equivalent capacity thickness; step S140 further includes: calculating the capacity thickness and signal coverage rate of the scenario to be evaluated based on the frequency point set of the filled data blocks; calculating the capacity continuity of the scenario to be evaluated based on the capacity continuity scoring table; calculating the equivalent capacity thickness of the scenario to be evaluated based on the capacity thickness, signal coverage rate, and capacity continuity.

[0052] Specifically, in order to evaluate network performance data such as network capacity, based on the data block frequency point set JZ_A and the capacity continuity scoring table, index data such as regional capacity thickness, capacity continuity, and equivalent capacity thickness are output. First, the formula for calculating the capacity thickness is as follows in Equation (1):

[0053] Capacity thickness = number of frequency points in the regional capacity layer / total regional distance labels; (1)

[0054] The capacity thickness reflects the thickness of the capacity at the regional level (which can be at the data block level, grid level, city level, provincial level, etc.). Taking a certain high-speed rail line in a certain province as an example, the schematic diagram of the calculation result of the capacity thickness is as Figure 8 shown.

[0055] Second, the calculation formula for capacity continuity is as follows in formula (2):

[0056] Capacity continuity = Accumulation of scoring values within the region / Number of scoring items; (2)

[0057] The capacity continuity reflects the continuity of signal coverage at the regional level (which can be at the data block level, grid level, city level, provincial level, etc.). Taking a certain high-speed rail line in a certain province as an example, the capacity continuity is calculated and statistically analyzed as Figure 9 shown.

[0058] Third, the calculation formula for signal coverage rate is as follows in formula (3):

[0059] Signal coverage rate (private network signal / total signal) = Number of data blocks of (private network signal / total signal) / Total number of blocks of (private network signal / total signal); (3)

[0060] Among them, the number of data blocks of (private network signal / total signal) refers to the data blocks that meet the conditions in step S110; in this embodiment, the signal coverage rates in the example data are all 100%.

[0061] Fourth, the calculation formula for equivalent capacity thickness is as follows in formula (4):

[0062] Equivalent capacity thickness = Signal coverage rate * Capacity thickness * Capacity continuity; (4)

[0063] The equivalent capacity thickness comprehensively reflects the signal coverage rate, capacity layer thickness, and capacity layer continuity at the regional level (which can be at the data block level, grid level, city level, provincial level) of the capacity layer. The statistical analysis of the equivalent capacity thickness in the example is as Figure 10 shown.

[0064] In an optional manner, step S140 further includes: performing terrain rendering on each data block according to the capacity thickness, capacity continuity, signal coverage rate, and equivalent capacity thickness of the to-be-evaluated scenario; associating the data blocks of the to-be-evaluated scenario with the corresponding sub-cells or POI devices; obtaining the network performance of the corresponding sub-cells or POI devices for each data block according to the terrain rendering result.

[0065] Specifically, in order to intuitively reflect the network performance, terrain rendering can be performed on each data block according to the capacity thickness, capacity continuity, signal coverage rate, and equivalent capacity thickness of the scenario to be evaluated, so as to present the capacity thickness of each data block. Each data block can be associated with a corresponding sub-cell or POI device to reflect the capacity coverage of each sub-cell or POI device, so that the problem of sudden thinning of the capacity thickness or sudden interruption of the capacity continuity under a certain sub-cell or POI device in the same cell can be monitored and located.

[0066] By adopting the method of this embodiment, by collecting the sweep data of the scenario to be evaluated, dividing the scenario to be evaluated into data blocks according to a preset distance, generating a set of data benchmark tables after processing the sweep data according to the data blocks; generating a data block frequency point set according to the set of data benchmark tables; extracting the frequency point information in the set of data benchmark tables and matching it with the data block frequency point set respectively, and filling the data block frequency point set according to the matching result; evaluating the network performance of the scenario to be evaluated according to the filled data block frequency point set to obtain a network performance evaluation result. Based on the analysis of the coverage interference situation in high-speed rail and subway scenarios using sweep data, this method processes the signals of the data blocks within the coverage distance threshold range through the sweep data, scores the capacity thickness and capacity continuity to evaluate the network performance, and outputs the equivalent capacity thickness in combination with the frequency point coverage situation, providing an evaluation method for the network capacity performance of the track area in special trunk line scenarios, and realizing the monitoring of the network capacity performance of sub-cells or POI devices in the same cell, and solving the technical problem that the capacity situation along the track area cannot be reflected according to the coverage in the prior art.

[0067] Figure 11 FIG. shows a schematic structural diagram of an embodiment of a network performance evaluation device based on sweep data according to the present invention. As Figure 11 shown, the device includes: a data set construction module 1110, a filling module 1120, and an evaluation module 1130.

[0068] The data set construction module 1110 is configured to collect the sweep data of the scenario to be evaluated, divide the scenario to be evaluated into data blocks according to a preset distance, generate a set of data benchmark tables after processing the sweep data according to the data blocks; generate a data block frequency point set according to the set of data benchmark tables.

[0069] In an optional manner, the sweep data at least includes the frequency point information, physical cell identification information, and signal strength information of the signal; the data set construction module 1110 is further configured to: for each data block, select the characteristic signal strength value of the data block according to the signal strength information of the signal within the data block; perform data processing on the sweep data according to the characteristic signal strength value, and generate a set of data benchmark tables according to the processed sweep data.

[0070] In an alternative manner, the data set construction module 1110 is further configured to: extract the frequency point information of the swept frequency data corresponding to multiple data blocks from the data reference table set, generate frequency point subsets of the multiple data blocks according to the data blocks; and construct a data block frequency point set according to the frequency point subsets of the multiple data blocks.

[0071] The filling module 1120 is configured to extract the frequency point information in the data reference table set and match it with the data block frequency point set respectively, and fill the data block frequency point set according to the matching result.

[0072] In an alternative manner, the filling module 1120 is further configured to: for each data block, extract the frequency point information corresponding to the data block in the data reference table set and match it with the frequency point subset of the data block in the data block frequency point set, and determine whether the frequency point information already exists in the frequency point subset of the data block. If so, perform a marking process on the frequency point information in the data reference table set; if not, write the frequency point information into the frequency point subset of the data block.

[0073] The evaluation module 1130 is configured to evaluate the network performance of the to-be-evaluated scenario according to the filled data block frequency point set, and obtain a network performance evaluation result.

[0074] In an alternative manner, the evaluation module 1130 is further configured to: for each frequency point, score the capacity continuity of the frequency point according to the occurrence of the frequency point in the frequency point subset of each data block, generate a capacity continuity score table according to the capacity continuity scores of each frequency point; calculate the index data of the to-be-evaluated scenario according to the capacity continuity score table and the filled data block frequency point set; and evaluate the network performance of the to-be-evaluated scenario according to the index data, and obtain a network performance evaluation result.

[0075] In an alternative manner, the index data includes one or more of the following data: capacity thickness, capacity continuity, signal coverage rate, equivalent capacity thickness; the evaluation module 1130 is further configured to: calculate the capacity thickness and signal coverage rate of the to-be-evaluated scenario according to the filled data block frequency point set; calculate the capacity continuity of the to-be-evaluated scenario according to the capacity continuity score table; and calculate the equivalent capacity thickness of the to-be-evaluated scenario according to the capacity thickness, signal coverage rate and capacity continuity.

[0076] In an alternative manner, the evaluation module 1130 is further configured to: perform terrain rendering on each data block according to the capacity thickness, capacity continuity, signal coverage rate and equivalent capacity thickness of the to-be-evaluated scenario; associate the data blocks of the to-be-evaluated scenario with the corresponding sub-cells or POI devices; and obtain the network performance of the sub-cells or POI devices corresponding to each data block according to the terrain rendering result.

[0077] Using the device of this embodiment, by collecting the sweep frequency data of the scene to be evaluated, dividing the scene to be evaluated into data blocks according to a preset distance, generating a set of data reference tables after processing the sweep frequency data according to the data blocks; generating a data block frequency point set according to the set of data reference tables; extracting the frequency point information in the set of data reference tables and respectively matching it with the data block frequency point set, and filling the data block frequency point set according to the matching result; evaluating the network performance of the scene to be evaluated according to the filled data block frequency point set to obtain a network performance evaluation result. Based on the analysis of the coverage interference situation in high-speed rail and subway scenarios using sweep frequency data, this device processes the signals of the data blocks within the coverage distance threshold range through the sweep frequency data, scores the capacity thickness and capacity continuity to evaluate the network performance, and outputs the equivalent capacity thickness in combination with the frequency point coverage situation, providing an evaluation method for the network capacity performance of the track area in special trunk line scenarios, and realizing the network capacity performance monitoring of sub-cells or POI devices under the same cell, solving the technical problem in the prior art that the capacity situation along the track area cannot be reflected according to the coverage.

[0078] An embodiment of the present invention provides a non-volatile computer storage medium, and the computer storage medium stores at least one executable instruction, and the computer executable instruction can execute a network performance evaluation method based on sweep frequency data in any of the above method embodiments.

[0079] The executable instruction can specifically be used to cause the processor to perform the following operations:

[0080] Collect the sweep frequency data of the scene to be evaluated, divide the scene to be evaluated into data blocks according to a preset distance, and generate a set of data reference tables after processing the sweep frequency data according to the data blocks;

[0081] Generate a data block frequency point set according to the set of data reference tables;

[0082] Extract the frequency point information in the set of data reference tables and respectively match it with the data block frequency point set, and fill the data block frequency point set according to the matching result;

[0083] Evaluate the network performance of the scene to be evaluated according to the filled data block frequency point set to obtain a network performance evaluation result.

[0084] Figure 12 The structural schematic diagram of an embodiment of the computing device of the present invention is shown, and the specific implementation of the computing device is not limited in the specific embodiment of the present invention.

[0085] As Figure 12 shown, the computing device may include:

[0086] A processor, a communications interface, a memory, and a communication bus.

[0087] Wherein: The processor, the communications interface, and the memory communicate with each other via the communication bus. The communications interface is used to communicate with network elements of other devices such as clients or other servers. The processor is used to execute a program, and specifically can execute the relevant steps in the above embodiments of a network performance evaluation method based on sweep data.

[0088] Specifically, the program may include program code, and the program code includes computer operation instructions.

[0089] The processor may be a central processing unit (CPU), or a specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present invention. One or more processors included in the server may be of the same type of processor, such as one or more CPUs; or may be of different types of processors, such as one or more CPUs and one or more ASICs.

[0090] The memory is used to store the program. The memory may include high-speed RAM memory, and may also include non-volatile memory, such as at least one disk memory.

[0091] Specifically, the program is specifically used to cause the processor to perform the following operations:

[0092] Collect sweep data of the scene to be evaluated, divide the scene to be evaluated into data blocks according to a preset distance, and generate a set of data reference tables after processing the sweep data according to the data blocks;

[0093] Generate a data block frequency point set based on the set of data reference tables;

[0094] Extract the frequency point information in the set of data reference tables and match it with the data block frequency point set respectively, and fill the data block frequency point set according to the matching results;

[0095] Evaluate the network performance of the scene to be evaluated based on the filled data block frequency point set, and obtain a network performance evaluation result.

[0096] The algorithms or displays provided herein are not inherently related to any particular computer, virtual system, or other device. A variety of general-purpose systems may also be used in conjunction with the teachings presented herein. The structure required to construct such systems will be apparent from the above description. Additionally, embodiments of the present invention are not directed to any particular programming language. It should be understood that the teachings of the present invention described herein can be implemented in a variety of programming languages, and the description of specific languages above is provided to disclose the best mode of the present invention.

[0097] In the specification provided herein, numerous specific details are set forth. However, it can be understood that embodiments of the present invention may be practiced without these specific details. In some instances, well-known methods, structures, and techniques have not been shown in detail so as not to obscure the understanding of this specification.

[0098] Similarly, it should be understood that, in order to streamline the present invention and assist in understanding one or more of the various inventive aspects, in the above description of the exemplary embodiments of the present invention, the various features of the embodiments of the present invention are sometimes grouped together in a single embodiment, figure, or description thereof. However, the disclosed method should not be construed as reflecting an intention that the claimed invention requires more features than are expressly recited in each claim. Rather, as reflected in the following claims, the inventive aspects lie in less than all of the features of the single foregoing disclosed embodiment. Thus, the claims following the detailed description are hereby expressly incorporated into the detailed description, with each claim standing on its own as a separate embodiment of the present invention.

[0099] Those skilled in the art will appreciate that the modules in the devices in the embodiments can be adaptively changed and disposed in one or more devices different from the embodiments. The modules or units or components in the embodiments can be combined into one module or unit or component, and in addition, they can be divided into multiple sub-modules or sub-units or sub-components. Except where at least some of such features and / or processes or units are mutually exclusive, any combination can be used to combine all the features disclosed in this specification (including the accompanying claims, abstract, and drawings) and all the processes or units of any method or device so disclosed. Unless otherwise expressly stated, each feature disclosed in this specification (including the accompanying claims, abstract, and drawings) can be replaced by an alternative feature that provides the same, equivalent, or similar purpose.

[0100] In addition, those skilled in the art can understand that although some embodiments herein include certain features included in other embodiments rather than other features, the combination of features of different embodiments means that it is within the scope of the present invention and forms different embodiments. For example, in the following claims, any one of the claimed embodiments can be used in any combination.

[0101] Each component embodiment of the present invention can be implemented in hardware, or in software modules running on one or more processors, or in a combination thereof. Those skilled in the art should understand that a microprocessor or a digital signal processor (DSP) can be used in practice to implement some or all of the functions of some or all of the components according to the embodiments of the present invention. The present invention can also be implemented as a device or apparatus program (such as a computer program and a computer program product) for executing some or all of the methods described herein. Such a program for implementing the present invention can be stored on a computer-readable medium, or can be in the form of one or more signals. Such signals can be downloaded from an Internet website, or provided on a carrier signal, or provided in any other form.

[0102] It should be noted that the above embodiments illustrate the present invention rather than limit the present invention, and those skilled in the art can design alternative embodiments without departing from the scope of the appended claims. In the claims, any reference signs placed between parentheses shall not be construed as limiting the claim. The word "comprising" does not exclude the presence of elements or steps not listed in the claim. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. The present invention can be implemented by means of hardware including several different elements and by means of a suitably programmed computer. In a unit claim listing several devices, several of these devices can be embodied by the same item of hardware. The use of the words first, second, and third, etc. does not denote any order. These words can be interpreted as names. The steps in the above embodiments, unless otherwise specified, should not be construed as limiting the order of execution.

Claims

1. A network performance evaluation method based on swept-frequency data, characterized in that Including: Collecting sweep frequency data of the scene to be evaluated, dividing the scene to be evaluated into data blocks according to a preset distance, and generating a set of data reference tables after processing the sweep frequency data according to the data blocks; Generating a data block frequency point set according to the set of data reference tables; Extracting the frequency point information in the set of data reference tables and matching them with the data block frequency point set respectively, and filling the data block frequency point set according to the matching results; Evaluating the network performance of the scene to be evaluated according to the filled data block frequency point set to obtain a network performance evaluation result.

2. The method according to claim 1, wherein The sweep frequency data at least includes frequency point information of the signal, physical cell identification information, and level intensity information; The generating a set of data reference tables after processing the sweep frequency data according to the data blocks further includes: For each data block, selecting a characteristic level value of the data block according to the level intensity information of the signal in the data block; Performing data processing on the sweep frequency data according to the characteristic level value, and generating a set of data reference tables according to the processed sweep frequency data.

3. The method according to claim 1 or 2, characterized in that, The generating a data block frequency point set according to the set of data reference tables further includes: Extracting the frequency point information of the sweep frequency data corresponding to multiple data blocks from the set of data reference tables, and generating subsets of frequency points of multiple data blocks according to the data blocks; Constructing a data block frequency point set according to the subsets of frequency points of multiple data blocks.

4. The method according to claim 3, characterized in that, The extracting the frequency point information in the set of data reference tables and matching them with the data block frequency point set respectively, and filling the data block frequency point set according to the matching results further includes: For each data block, extracting the frequency point information corresponding to the data block in the set of data reference tables and matching it with the subset of frequency points of the data block in the data block frequency point set, and judging whether the frequency point information already exists in the subset of frequency points of the data block. If so, performing a marking process on the frequency point information in the set of data reference tables; If not, writing the frequency point information into the subset of frequency points of the data block.

5. The method according to claim 4, characterized in that, The evaluating the network performance of the scene to be evaluated according to the filled data block frequency point set to obtain a network performance evaluation result further includes: For each frequency point, scoring the capacity continuity of the frequency point according to the occurrence situation of the frequency point in the subsets of frequency points of each data block, and generating a capacity continuity scoring table according to the capacity continuity scores of each frequency point; Calculating the index data of the scene to be evaluated according to the capacity continuity scoring table and the filled data block frequency point set; Evaluating the network performance of the scene to be evaluated according to the index data to obtain a network performance evaluation result.

6. The method according to claim 5, wherein The index data includes one or more of the following data: capacity thickness, capacity continuity, signal coverage rate, equivalent capacity thickness; The calculating the index data of the scene to be evaluated according to the capacity continuity scoring table and the filled data block frequency point set further includes: Calculating the capacity thickness and signal coverage rate of the scene to be evaluated according to the filled data block frequency point set; Calculating the capacity continuity of the scene to be evaluated according to the capacity continuity scoring table; Calculate the equivalent capacity thickness of the to-be-evaluated scenario according to the capacity thickness, the signal coverage rate, and the capacity continuity.

7. The method according to claim 6, characterized in that, Evaluating the network performance of the to-be-evaluated scenario according to the index data to obtain a network performance evaluation result further includes: Perform terrain rendering on each data block according to the capacity thickness, capacity continuity, signal coverage rate, and equivalent capacity thickness of the to-be-evaluated scenario; Associate the data blocks of the to-be-evaluated scenario with the corresponding sub-cells or POI devices; Obtain the network performance of the corresponding sub-cells or POI devices of each data block according to the terrain rendering result.

8. A network performance evaluation device based on sweep data, characterized in that Include: A data set construction module, configured to collect sweep data of the to-be-evaluated scenario, divide the to-be-evaluated scenario into data blocks according to a preset distance, and generate a data reference table set after processing the sweep data according to the data blocks; Generate a data block frequency point set according to the data reference table set; A filling module, configured to extract the frequency point information in the data reference table set and match it with the data block frequency point set respectively, and fill the data block frequency point set according to the matching result; An evaluation module, configured to evaluate the network performance of the to-be-evaluated scenario according to the filled data block frequency point set to obtain a network performance evaluation result.

9. A computing device, characterized in that, Include: A processor, a memory, a communication interface, and a communication bus, and the processor, the memory, and the communication interface complete communication with each other through the communication bus; The memory is used to store at least one executable instruction, and the executable instruction causes the processor to perform the operations corresponding to a network performance evaluation method according to any one of claims 1-7.

10. A computer storage medium, characterized in that, At least one executable instruction is stored in the storage medium, and the executable instruction causes the processor to perform the operations corresponding to a network performance evaluation method according to any one of claims 1-7.

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